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Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework

Author

Listed:
  • Mengli Zhang

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Xianglong Fan

    (Agricultural College, Shihezi University, Shihezi 832003, China)

  • Pan Gao

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Li Guo

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Xuanrong Huang

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Xiuwen Gao

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Jinpeng Pang

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

  • Fei Tan

    (College of Information Science and Technology, Shihezi University, Shihezi 832061, China)

Abstract

Soil salinization affects agricultural productivity and ecosystem health in Xinjiang, especially in arid areas. The region’s complex topography and limited agricultural data emphasize the pressing need for effective, large-scale monitoring technologies. Therefore, 1044 soil samples were collected from arid farmland in northern Xinjiang, and the potential effectiveness of soil salinity monitoring was explored by combining environmental variables with Landsat 8 and Sentinel-2. The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. The results suggest that fertilizer use plays a critical role in soil salinization processes. Notably, the interpretable model KAN achieved an accuracy of 0.75 in correctly classifying the degree of soil salinity. This study highlights the potential of integrating multi-source remote sensing data with deep learning technologies, offering a pathway to large-scale soil salinity monitoring, and thereby providing valuable support for soil management.

Suggested Citation

  • Mengli Zhang & Xianglong Fan & Pan Gao & Li Guo & Xuanrong Huang & Xiuwen Gao & Jinpeng Pang & Fei Tan, 2025. "Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework," Land, MDPI, vol. 14(1), pages 1-27, January.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:1:p:110-:d:1562454
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    References listed on IDEAS

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    1. Ruiliang Liu & Keli Jia & Haoyu Li & Junhua Zhang, 2024. "Using Unmanned Aerial Vehicle Data to Improve Satellite Inversion: A Study on Soil Salinity," Land, MDPI, vol. 13(9), pages 1-19, September.
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